Improving the Effectiveness of MCMC through Adaptation

Citation:
Milgo E, Ronoh N, Wagacha PW, Manderick B. "Improving the Effectiveness of MCMC through Adaptation." https://www.kuleuven-kulak.be/benelearn/papers/Benelearn_2016_paper_10.pdf. 1996.

Abstract:

The Metropolis-Hastings Markov Chain Monte Carlo algorithm uses a proposal
distribution to sample from a given target distribution. The proposal distribution has to be
tuned beforehand which is an expensive exercise. However, adaptive algorithms
automatically tune the proposal distribution based on knowledge from past samples,
reducing the tuning cost. In this study we first examine Adaptive Metropolis, Metropolis
Gaussian Adaptation and Covariance Matrix Adaptation Evolutionary Strategies.

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